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Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer
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作者 Yuan Liu Xiao-Ning Lu +3 位作者 Hui-Ming Guo Chan Bao Juan Zhang yu-ni jin 《World Journal of Clinical Cases》 SCIE 2024年第18期3385-3394,共10页
BACKGROUND Endometrial cancer(EC)is a common gynecological malignancy that typically requires prompt surgical intervention;however,the advantage of surgical management is limited by the high postoperative recurrence r... BACKGROUND Endometrial cancer(EC)is a common gynecological malignancy that typically requires prompt surgical intervention;however,the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes.Previous studies have highlighted the prognostic potential of circulating tumor DNA(ctDNA)monitoring for minimal residual disease in patients with EC.AIM To develop and validate an optimized ctDNA-based model for predicting shortterm postoperative EC recurrence.METHODS We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model,which was validated on 143 EC patients operated between 2020 and 2021.Prognostic factors were identified using univariate Cox,Lasso,and multivariate Cox regressions.A nomogram was created to predict the 1,1.5,and 2-year recurrence-free survival(RFS).Model performance was assessed via receiver operating characteristic(ROC),calibration,and decision curve analyses(DCA),leading to a recurrence risk stratification system.RESULTS Based on the regression analysis and the nomogram created,patients with postoperative ctDNA-negativity,postoperative carcinoembryonic antigen 125(CA125)levels of<19 U/mL,and grade G1 tumors had improved RFS after surgery.The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis,calibration curves,and DCA methods,highlighting its high accuracy and clinical utility.Furthermore,using the nomogram,the patients were successfully classified into three risk subgroups.CONCLUSION The nomogram accurately predicted RFS after EC surgery at 1,1.5,and 2 years.This model will help clinicians personalize treatments,stratify risks,and enhance clinical outcomes for patients with EC. 展开更多
关键词 Circulating tumor DNA Endometrial cancer Short-term recurrence Predictive model Prospective validation
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基于Logistic回归的外阴上皮内瘤变危险度评价模型的构建 被引量:2
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作者 金钰铌 李亚敏 +2 位作者 刘菀 许静 鲁潇凝 《中国现代医学杂志》 CAS 北大核心 2021年第15期36-41,共6页
目的应用临床资料和流行病学资料建立外阴上皮内瘤变危险度评价模型。方法模型样本为101例外阴上皮内瘤变患者、134例外阴良性病变者及150例外阴完全正常者。对其临床资料和流行病学资料进行单因素分析,通过受试者工作特征(ROC)曲线获... 目的应用临床资料和流行病学资料建立外阴上皮内瘤变危险度评价模型。方法模型样本为101例外阴上皮内瘤变患者、134例外阴良性病变者及150例外阴完全正常者。对其临床资料和流行病学资料进行单因素分析,通过受试者工作特征(ROC)曲线获取影响因素的截断值,构建Logistic回归模型,采用混淆矩阵回代分析验证模型的正确率。结果经Logistic回归分析,年龄、吸烟、人类乳头瘤病毒(HPV)感染、外阴瘙痒、外阴触血、外阴肿物、外阴疼痛、绝经与否、中性粒细胞绝对值(GRA)、中性粒细胞与淋巴细胞的比值(NLR)、血清糖类抗原125(CA125)指标进入回归方程。回归模型最大似然比(LR)检验具有差异(P<0.05)。混淆矩阵回代分析结果发现该模型总体预测正确率为78.44%(302/385)。结论应用临床资料和流行病学资料构建外阴上皮内瘤变危险度评价模型,能有效提高外阴上皮内瘤变正确筛查率。 展开更多
关键词 外阴上皮内瘤变 早期筛查 LOGISTIC回归 危险度评价
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